Big data maturity model: Difference between revisions

Revision as of 16:07, 31 May 2019

Revision as of 03:58, 8 July 2019

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# To help guide development milestones

# To avoid pitfalls in establishing and building big data capabilities

Key organizational areas refer to “People, Process and Technology” and the subcomponents include<ref>{{Cite web|url=http://ibmdatamag.com/2014/09/measuring-maturity-of-big-data-initiatives/|title=Measuring maturity of big data initiatives|last=Krishnan|first=|date=2014|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref> alignment, architecture, data, [[data governance]], delivery, development, measurement, program governance, scope, skills, sponsorship, [[statistical model]]ling, technology, value and visualization.

The stages or phases in BDMMs depict the various ways in which data can be used in an organization and is one of the key tools to set direction and monitor the health of organization’s big data programs.<ref name=":2">{{Cite journal|last=El-Darwiche |display-authors=etal |first=|date=2014|title=Big Data Maturity: An action plan for policymakers and executives|url=|journal=World Economic Forum|volume=|pages=|via=}}</ref><ref name=":3">{{Cite web|url=http://www.radcliffeadvisory.com/research/download.php?file=RAS_BD_MatMod.pdf|title=Leverage a Big Data Maturity model to build your big data roadmap|last=|first=|date=2014|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref>

An underlying assumption is that a high level of big data maturity correlates with an increase in revenue and reduction in operational expense. However, reaching the highest level of maturity involves major investments over many years.<ref name=":0">{{Cite journal|last=Halper|first=Fern|date=2016|title=A Guide to Achieving Big Data Analytics Maturity|url=|journal=TDWI Benchmark guideGuide|volume=|pages=|via=}}</ref> Only a few companies are considered to be at a “Mature” stage of big data and analytics. These include internet-based companies (such as [[LinkedIn]], [[Facebook]], and [[Amazon.com|Amazon]]) and other non-internet-based companies, including financial institutions (fraud analysis, real-time customer messaging and behavioral modeling) and retail organizations ([[Clickstream|click-stream]] analytics together with self-service analytics for teams).<ref name=":0" />

== Categories of Big Data Maturity Models ==

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Comparative big data maturity models aim to benchmark an organization in relation to its industry peers and normally consist of a survey containing quantitative and qualitative information.

=== CSC Big Data Maturity Tool<ref>{{Cite web|url=http://csc.bigdatamaturity.com/|title=CSC Big Data Maturity Tool: Business Value, Drivers, and Challenges|last=Inc.|first=Creative services by Cyclone Interactive Multimedia Group, Inc. (www.cycloneinteractive.com) Site designed and hosted by Cyclone Interactive Multimedia Group,|website=csc.bigdatamaturity.com|language=en|access-date=2017-05-21}}</ref> ===

The CSC Big Data maturity tool acts as a comparative tool to benchmark an organization’s big data maturity. A survey is undertaken and the results are then compared to other organizations within a specific industry and within the wider market.

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* Stage 4: Business model transformation

=== Van Veenstra's Model <ref>{{Cite journal|last=van Veenstra|first=Anne Fleur|date=|title=Big Data in Small Steps: Assessing the value of data|url=http://www.idnext.eu/files/TNO-whitepaper--Big-data-in-small-steps.pdf|journal=White paperPaper|volume=|pages=|via=}}</ref> ===

The prescriptive model proposed by Van Veenstra aims to firstly explore the existing big data environment of the organization followed by exploitation opportunities and a growth path towards big data maturity. The model makes use of four phases namely:

* Efficiency